Instructions to use vpadaraju/misafetychatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vpadaraju/misafetychatbot with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vpadaraju/misafetychatbot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vpadaraju/misafetychatbot")
model = AutoModelForCausalLM.from_pretrained("vpadaraju/misafetychatbot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vpadaraju/misafetychatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vpadaraju/misafetychatbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vpadaraju/misafetychatbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vpadaraju/misafetychatbot

SGLang

How to use vpadaraju/misafetychatbot with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vpadaraju/misafetychatbot" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vpadaraju/misafetychatbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vpadaraju/misafetychatbot" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vpadaraju/misafetychatbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vpadaraju/misafetychatbot with Docker Model Runner:
```
docker model run hf.co/vpadaraju/misafetychatbot
```

misafetychatbot

This model is a fine-tuned version of microsoft/DialoGPT-small on the empathetic_dialogues dataset. It achieves the following results on the evaluation set:

Loss: 2.9995

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.1961	1.0	2397	3.0838
3.0583	2.0	4794	3.0121
2.9961	2.9990	7188	2.9995

Framework versions

Transformers 4.48.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for vpadaraju/misafetychatbot

Base model

microsoft/DialoGPT-small

Finetuned

(54)

this model

vpadaraju
/

misafetychatbot

misafetychatbot

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for vpadaraju/misafetychatbot

Dataset used to train vpadaraju/misafetychatbot